
Topics 
Lecture Notes 
Remarks 
1 
Introduction, What is Data and Model, Machine Learning Workflow, Distance Based Classifiers, Bayes Decision Theory 
slides1 

2 
Different types of Learning, Supervised Learning, Foundational Aspects of ML, Linear Regression 
slides2 

3 
Probabilistic view of Linear Regression, Logistic Regression, Hyperplane based Classifiers and Perceptron 
slides3 

4 
Support Vector Machines, Kernel Methods 
slides4 

5 
Feed Forward Neural Networks, Backpropagation algorithm, CNNs, RNNs 
slides5 

6 
Unsupervised Learning, Dimentionality Reduction, KMeans Clustering 
slides6 

7 
Spectral Clustering 
slides7 

8 
Probabilistic Models, Graphical Models, Markov Random Fields, Markov Chain, Monte Carlo Methods, Restricted Boltzmann Machines 
see lecutre video 

9 
Latent Variable Models, Gaussian Mixture Models, Free Energy Optimization, Expectation Maximization algorithm 
see lecutre video 

10 
Model Selection, Making ML algorithms work 
slides8 
